pandas-dev
BUG: `pd.Series.where` incorrectly casts `<NA>` to float `NaN`
Pandas version checks
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[x] I have checked that this issue has not already been reported.
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[x] I have confirmed this bug exists on the latest version of pandas.
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[x] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
print(pd.__version__)
t1 = pd.DataFrame(
{
'c0': ['aa'],
'c1': ['bb']
}
)
result = t1.assign(c0_t1=lambda df: df['c0'].where(t1['c0'].isin(['c1']), other=pd.NA))
print(result)
print(result['c0_t1'].apply(type))
2.3.3
c0 c1 c0_t1
0 aa bb <NA>
0 <class 'pandas._libs.missing.NAType'>
Name: c0_t1, dtype: object
3.0.0.dev0+2777.g8813fafe66
c0 c1 c0_t1
0 aa bb NaN
0 <class 'float'>
Name: c0_t1, dtype: object
Issue Description
When using .where(..., other=pd.NA) on a string column, pandas 3.0.0.dev0+2777.g8813fafe66 incorrectly casts the output to float and replaces <NA> with NaN.
This behavior is different from pandas 2.3.3.
Expected Behavior
c0 c1 c0_t1
0 aa bb <NA>
0 <class 'pandas._libs.missing.NAType'>
Name: c0_t1, dtype: object
Installed Versions
INSTALLED VERSIONS
------------------
commit : 9c8bc3e55188c8aff37207a74f1dd144980b8874
python : 3.10.19
python-bits : 64
OS : Linux
OS-release : 6.14.0-35-generic
Version : #35~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 13:55:17 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.3.3
numpy : 1.26.4
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.3
Cython : None
sphinx : None
IPython : 8.27.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.14.2
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.10.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.7
numba : 0.61.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 22.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.3
sqlalchemy : 2.0.44
tables : None
tabulate : 0.9.0
xarray : 2025.6.1
xlrd : None
xlsxwriter : None
zstandard : 0.25.0
tzdata : 2025.2
qtpy : None
pyqt5 : None
INSTALLED VERSIONS
------------------
commit : 8813fafe66a361f652dc5d83e41ec11f8055725c
python : 3.12.0
python-bits : 64
OS : Linux
OS-release : 6.14.0-35-generic
Version : #35~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 13:55:17 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 3.0.0.dev0+2777.g8813fafe66
numpy : 1.26.4
dateutil : 2.9.0.post0
pip : 25.3
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
psycopg2 : None
pymysql : None
pyarrow : None
pyiceberg : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
qtpy : None
pyqt5 : None
This is due to the new string dtype.
https://pandas.pydata.org/docs/dev/user_guide/migration-3-strings.html#the-missing-value-sentinel-is-now-always-nan
t1 = pd.DataFrame(
{
'c0': ['aa'],
'c1': ['bb']
}
)
print(t1.dtypes)
# c0 str
# c1 str
# dtype: object
If you want <NA> as the NA-sentinel, you can opt into the string dtype.
t1 = pd.DataFrame(
{
'c0': ['aa'],
'c1': ['bb']
},
dtype="string",
)
result = t1.assign(c0_t1=lambda df: df['c0'].where(t1['c0'].isin(['c1']), other=pd.NA))
print(result)
# c0 c1 c0_t1
# 0 aa bb <NA>
You can recover 2.3.x behavior by setting pd.set_option("future.infer_string", False), however this will eventually be going away.
